생명과학과_I-Bio 신임교원 채용 후보 공개세미나 (2)
[Life Sciences_I-Bio Faculty Candidate Seminar Notice]
            
              
          ▶Subject: Computational approaches for analyzing big data in human genetics
            
          ▶Speaker: Jae Hoon Sul, Ph.D.(Harvard Medical School)
                   
          ▶Date: 10:00~11:00 AM/July 22(Wed.)/2015
             
          ▶Place: Auditorium(1F), Postech Biotech Center
            
                  *Abctract
                In the past decade, microarray and next-generation sequencing technologies have generated an enormous amount of data to discover genetic variants in human genomes and to find genetic basis of diseases. The technologies have shifted a paradigm of genetic studies from studies that analyzed fewer than a hundred individuals at hundreds of markers to studies that analyze tens of thousand individuals at millions of genetic variants. With rapid decrease in sequencing costs and emphasis on genomic medicine, studies will sequence hundreds of thousands of individuals in the near future. The large genetic data, however, have introduced two major challenges. The first challenge is developing computational methods that can utilize this big data efficiently. The second challenge is an actual analysis of the big data because it has become increasingly complicated to incorporate all methods and correctly analyze the big data.
In this talk, I will describe my research work to address the two challenges. First, I will introduce a statistical approach to efficiently correct for population structure in genome-wide association studies. Population structure may cause spurious associations if cases and controls are sampled from different populations. I will show that my method correctly removes effects of population structure and reduces a computational time from years to hours. Next, I will discuss a method to perform multiple testing correction rapidly in expression quantitative trait loci (eQTL) studies that attempt to identify genes whose expressions are influenced by genetic variants. As eQTL studies have grown larger in sample size, multiple testing correction using a permutation test has become a computational bottleneck. I developed a multivariate normal sampling approach that is more than 100 times faster than the permutation test for the sample size of 2,000. My approach will be adopted by Genotype-Tissue Expression (GTEx), a large consortium aiming to obtain gene expression from many human tissues. I will also present a novel approach to detect rare variants associated with a disease in large families. Family-based studies have attracted great attention recently because of their higher power for rare variant testing than case-control studies. I developed a method called RareIBD that can be applied to large pedigrees, both binary and quantitative traits, and affected-only pedigrees. Using simulations, I will show my method achieves higher power than previous approaches. Lastly, I will discuss my work on analyzing high-coverage whole genome sequencing (WGS) of 808 ADNI individuals. I will present a challenge in analyzing large WGS data and procedures to measure the quality of WGS

          ▶Inquiry: Dept. of Life Sciences Tel: 279-2721, 8181